Biomedical Engineering and Telemedicine Centre (GBT), ETSI Telecomunicación, Center for Biomedical Technology, Universidad Politécnica de Madrid (UPM), Avda Complutense, 30, 28040, Madrid, Spain.
Department of Surgery, Faculty of Medicine, Universidad Nacional Autónoma de México (UNAM), Circuito Interior, Av. Universidad 3000, Ciudad Universitaria, Coyoacán, 04510, Mexico City, Mexico.
Surg Endosc. 2018 Jul;32(7):3096-3107. doi: 10.1007/s00464-018-6022-6. Epub 2018 Jan 18.
Motion analysis parameters (MAPs) have been extensively validated for assessment of minimally invasive surgical skills. However, there are discrepancies on how specific MAPs, tasks, and skills match with each other, reflecting that motion analysis cannot be generalized independently of the learning outcomes of a task. Additionally, there is a lack of knowledge on the meaning of motion analysis in terms of surgical skills, making difficult the provision of meaningful, didactic feedback. In this study, new higher significance MAPs (HSMAPs) are proposed, validated, and discussed for the assessment of technical skills in box trainers, based on principal component analysis (PCA).
Motion analysis data were collected from 25 volunteers performing three box trainer tasks (peg grasping/PG, pattern cutting/PC, knot suturing/KS) using the EVA tracking system. PCA was applied on 10 MAPs for each task and hand. Principal components were trimmed to those accounting for an explained variance > 80% to define the HSMAPs. Individual contributions of MAPs to HSMAPs were obtained by loading analysis and varimax rotation. Construct validity of the new HSMAPs was carried out at two levels of experience based on number of surgeries.
Three new HSMAPs per hand were defined for PG and PC tasks, and two per hand for KS task. PG presented validity for HSMAPs related to insecurity and economy of space. PC showed validity for HSMAPs related to cutting efficacy, peripheral unawareness, and confidence. Finally, KS presented validity for HSMAPs related with economy of space and knotting security.
PCA-defined HSMAPs can be used for technical skills' assessment. Construct validation and expert knowledge can be combined to infer how competences are acquired in box trainer tasks. These findings can be exploited to provide residents with meaningful feedback on performance. Future works will compare the new HSMAPs with valid scoring systems such as GOALS.
运动分析参数（MAPs）已广泛用于微创外科技能评估。然而，特定的 MAPs、任务和技能之间如何相互匹配存在差异，这反映出运动分析不能独立于任务的学习结果进行概括。此外，对于手术技能中的运动分析的含义知之甚少，这使得提供有意义的教学反馈变得困难。在这项研究中，基于主成分分析（PCA），提出、验证和讨论了新的更高意义运动分析参数（HSMAPs），用于箱式训练器的技术技能评估。
使用 EVA 跟踪系统，从 25 名志愿者执行三个箱式训练器任务（peg grasping/PG、pattern cutting/PC、knot suturing/KS）中收集运动分析数据。针对每个任务和手，对 10 个 MAPs 进行 PCA。将主成分修剪至解释方差>80%，以定义 HSMAPs。通过加载分析和方差极大旋转获得 MAPs 对 HSMAPs 的个体贡献。基于手术次数，在两个经验水平上进行新的 HSMAPs 的结构有效性研究。
PG 和 PC 任务的每只手定义了三个新的 HSMAPs，KS 任务的每只手定义了两个新的 HSMAPs。PG 呈现出与不安全和空间经济性相关的 HSMAPs 的有效性。PC 显示出与切割效果、周围无意识和信心相关的 HSMAPs 的有效性。最后，KS 呈现出与空间经济性和结安全性相关的 HSMAPs 的有效性。
基于 PCA 定义的 HSMAPs 可用于技术技能评估。结构验证和专家知识可以结合起来推断在箱式训练器任务中是如何获得能力的。这些发现可以被利用来为住院医师提供关于绩效的有意义的反馈。未来的工作将比较新的 HSMAPs 与 GOALS 等有效评分系统。
